hadsed / PyGPMLLinks
Gaussian process regression code.
☆24Updated 11 years ago
Alternatives and similar repositories for PyGPML
Users that are interested in PyGPML are comparing it to the libraries listed below
Sorting:
- Parametric Gaussian Process Regression for Big Data☆45Updated 5 years ago
- Provides various extensions to the GPML toolbox for Gaussian process inference in MATLAB.☆32Updated 8 years ago
- Variational Fourier Features☆85Updated 3 years ago
- A collection of Gaussian process models☆30Updated 7 years ago
- Code for AutoGP☆26Updated 5 years ago
- Structurally efficient multi-output linearly coregionalized Gaussian Processes: it's tricky, tricky, tricky, tricky, tricky.☆38Updated 2 years ago
- Deep Gaussian Processes in matlab☆93Updated 3 years ago
- Somewhat fast updating and downdating of Cholesky factors in Python☆33Updated last year
- Collapsed Variational Bayes☆71Updated 5 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- Look Ahead Hamiltonian Monte Carlo☆30Updated 10 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- 🤿 Implementation of doubly stochastic deep Gaussian Process using GPflow and TensorFlow 2.0☆27Updated last year
- Code for the paper 'Efficient Variational Inference for Gaussian Process Regression Networks'☆22Updated 11 years ago
- see https://github.com/thangbui/geepee for a faster implementation☆37Updated 8 years ago
- Deep Gaussian Processes with Importance-Weighted Variational Inference☆39Updated 6 years ago
- Implementation of the Gaussian Process Autoregressive Regression Model☆66Updated 4 months ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- Bayesian Linear Regression for python☆35Updated 11 years ago
- Additional kernels that can be used with scikit-learn's Gaussian Process module☆82Updated 10 months ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.☆43Updated 11 years ago
- Stochastic Gradient Riemannian Langevin Dynamics☆33Updated 10 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 3 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.☆22Updated 11 years ago
- ☆40Updated 6 years ago
- Convolutional Gaussian processes based on GPflow.☆96Updated 7 years ago
- ☆28Updated 6 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆44Updated 7 years ago